Quantum Entanglement-Based Differential Privacy

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores a novel approach to differential privacy utilizing quantum entanglement. Traditional differential privacy relies on adding noise to queries to obscure individual data contributions, a technique often limited by the dimensionality of the data and the potential for sophisticated attacks. We propose encoding data points into entangled qubit states, thereby leveraging the inherent correlations provided by entanglement to achieve stronger privacy guarantees. The core idea involves performing computations on these entangled states, minimizing the need for direct data exposure. Crucially, we introduce a mechanism for carefully calibrating measurement noise, exploiting the unique properties of entanglement to control the privacy loss. Our framework demonstrates the potential to significantly improve privacy levels compared to classical methods, especially when dealing with high-dimensional datasets, offering a new paradigm for privacy-preserving data analysis in the quantum realm. The theoretical analysis presents a novel framework for quantifying privacy loss, considering both the entanglement resource requirements and the calibrated noise levels. This work lays the foundation for future research into quantum-enhanced differential privacy and its implications for secure data processing.

DOI: 10.5281/zenodo.22335186

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